PROJECT SUMMARY Craniofacial anomalies (CFA) are a heterogeneous group of congenital anomalies that collectively represent the most common form of human structural birth defects. Proper craniofacial development requires transcription factors to establish cell identities for all participating cells in a precisely orchestrated spatial and temporal manner. Although half of the ~1,500 transcription factors are expressed in any given cell/tissue-type, only a handful of master transcription factors (MTFs) are required to establish or change a cell?s identity. Our long term goal is to identify MTFs and elucidate gene regulatory networks that control development of craniofacial regions. The objective of this application is to identify MTFs candidates by our computational model, MTFinder, with FaceBase?s transcriptome and epigenome data of human cranial Neural Crest Cells (cNCCs) and sub-regions of the developing mouse face at three critical stages. Current computational methods for MTFs prediction suffer from high false positive rates because they only use transcriptome data. Fortunately, recent studies have found that MTFs also have distinct epigenetic features. We developed a MTFs prediction method termed ?MTFinder? that incorporates both transcriptome and epigenome data into a Bayesian statistics model. Our preliminary studies show that MTFinder successfully rank all known MTFs of mouse embryonic stem cell(mESC) and liver hepatocytes within top 20 out of all 1,500 transcription factors with their transcriptome (RNA- seq) and epigenome (H3K27ac ChIP-seq) data. In this project, we will first evaluate and determine the optimal configuration of MTFinder in various cell/tissue-types (Aim 1). And then apply the optimal MTFinder configuration to FaceBase?s transcriptome and epigenome data to identify potential MTFs for craniofacial cell/tissue-types including cNCCs and sub-regions of the developing mouse face at three critical stages of development (Aim 2). At the completion of this project, we will generate a list of known and novel MTFs candidates for craniofacial cell/tissue-types that are critical for craniofacial development. These results will not only significantly advance our understanding of transcriptional regulation during craniofacial development but also provide potential MTFs for cell linage reprogramming, which holds great promise for regenerative medicine, disease modeling, drug screening and other applications.